from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HpMatchReporting(other_library="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=brute.

WARNING! Mismatch between validation scores for 14 prediction (58.33%). The observed differences can be found in the diff_accuracy_scores column. The chosen difference threshold is 0.001 for accuracy_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	diff_accuracy_scores
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	e70d8e7886766d41f30889506baa1e67	2cf49829391aa7891e877f2ed070adf0	2.299420	0.185296	NaN	0.000348	0.002299	brute	-1	1	0.663	0.463811	0.007244	0.687	4.957669	4.958274	0.024
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	22d5eac4a4146149b35eae6914921514	2cf49829391aa7891e877f2ed070adf0	3.125257	0.066843	NaN	0.000256	0.003125	brute	-1	5	0.757	0.467957	0.011975	0.742	6.678509	6.680695	0.015
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	16f94fc93942ff7ece860c9b0f64645f	2cf49829391aa7891e877f2ed070adf0	2.444856	0.014145	NaN	0.000327	0.002445	brute	1	100	0.882	0.520501	0.007273	0.875	4.697122	4.697580	0.007
8	KNeighborsClassifier_brute_force	predict	100000	1	100	16f94fc93942ff7ece860c9b0f64645f	2cf49829391aa7891e877f2ed070adf0	0.025754	0.001862	NaN	0.000031	0.025754	brute	1	100	1.000	0.010896	0.000573	0.000	2.363572	2.366836	1.000
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2d5f2b2c02d77766434d9e5033b4a76c	2cf49829391aa7891e877f2ed070adf0	3.145997	0.066497	NaN	0.000254	0.003146	brute	-1	100	0.882	0.519073	0.008674	0.875	6.060799	6.061645	0.007
11	KNeighborsClassifier_brute_force	predict	100000	1	100	2d5f2b2c02d77766434d9e5033b4a76c	2cf49829391aa7891e877f2ed070adf0	0.032688	0.003963	NaN	0.000024	0.032688	brute	-1	100	1.000	0.013126	0.003514	0.000	2.490363	2.578088	1.000
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	efac583623711d395ddae7a1e881ff68	2cf49829391aa7891e877f2ed070adf0	2.457043	0.038789	NaN	0.000326	0.002457	brute	1	5	0.757	0.464816	0.005783	0.742	5.286054	5.286463	0.015
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	b79d63bb7670c492de5c3befac58fe29	2cf49829391aa7891e877f2ed070adf0	1.616419	0.026886	NaN	0.000495	0.001616	brute	1	1	0.663	0.462349	0.008985	0.687	3.496101	3.496761	0.024
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	e70d8e7886766d41f30889506baa1e67	88843f54689e3271092f70126e1de585	1.822141	0.023534	NaN	0.000009	0.001822	brute	-1	1	0.896	0.100050	0.003502	0.967	18.212233	18.223385	0.071
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	22d5eac4a4146149b35eae6914921514	88843f54689e3271092f70126e1de585	2.731999	0.022395	NaN	0.000006	0.002732	brute	-1	5	0.922	0.102212	0.004119	0.974	26.728792	26.750488	0.052
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	16f94fc93942ff7ece860c9b0f64645f	88843f54689e3271092f70126e1de585	2.164288	0.018295	NaN	0.000007	0.002164	brute	1	100	0.929	0.156137	0.005102	0.975	13.861484	13.868882	0.046
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2d5f2b2c02d77766434d9e5033b4a76c	88843f54689e3271092f70126e1de585	2.765121	0.046763	NaN	0.000006	0.002765	brute	-1	100	0.929	0.156118	0.005172	0.975	17.711765	17.721482	0.046
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	efac583623711d395ddae7a1e881ff68	88843f54689e3271092f70126e1de585	2.152489	0.017996	NaN	0.000007	0.002152	brute	1	5	0.922	0.100269	0.001218	0.974	21.467215	21.468798	0.052
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	b79d63bb7670c492de5c3befac58fe29	88843f54689e3271092f70126e1de585	1.289950	0.012311	NaN	0.000012	0.001290	brute	1	1	0.896	0.100119	0.002262	0.967	12.884104	12.887391	0.071

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.014	0.001	5.674	0.0	-1	1	0.060	0.006	0.234	0.235	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.014	0.001	5.768	0.0	-1	5	0.057	0.001	0.243	0.243	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.014	0.001	5.912	0.0	1	100	0.058	0.001	0.233	0.233	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.014	0.001	5.843	0.0	-1	100	0.062	0.007	0.219	0.221	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.015	0.001	5.332	0.0	1	5	0.057	0.001	0.264	0.264	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.014	0.001	5.761	0.0	1	1	0.058	0.001	0.241	0.241	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.344	0.0	-1	1	0.009	0.001	0.533	0.534	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.310	0.0	-1	5	0.009	0.000	0.572	0.573	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.332	0.0	1	100	0.009	0.000	0.515	0.515	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.001	0.300	0.0	-1	100	0.008	0.001	0.631	0.634	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.322	0.0	1	5	0.008	0.001	0.610	0.612	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.334	0.0	1	1	0.009	0.000	0.560	0.561	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.299	0.185	0.0	0.002	-1	1	0.464	0.007	4.958	4.958	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.029	0.003	0.0	0.029	-1	1	0.011	0.001	2.536	2.542	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	3.125	0.067	0.0	0.003	-1	5	0.468	0.012	6.679	6.681	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.030	0.004	0.0	0.030	-1	5	0.013	0.002	2.436	2.455	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.445	0.014	0.0	0.002	1	100	0.521	0.007	4.697	4.698	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.026	0.002	0.0	0.026	1	100	0.011	0.001	2.364	2.367	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	3.146	0.066	0.0	0.003	-1	100	0.519	0.009	6.061	6.062	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.033	0.004	0.0	0.033	-1	100	0.013	0.004	2.490	2.578	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.457	0.039	0.0	0.002	1	5	0.465	0.006	5.286	5.286	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.025	0.001	0.0	0.025	1	5	0.012	0.001	2.148	2.158	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.616	0.027	0.0	0.002	1	1	0.462	0.009	3.496	3.497	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.024	0.001	0.0	0.024	1	1	0.011	0.001	2.164	2.170	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.822	0.024	0.0	0.002	-1	1	0.100	0.004	18.212	18.223	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.004	0.0	0.006	-1	1	0.001	0.000	9.253	9.331	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.732	0.022	0.0	0.003	-1	5	0.102	0.004	26.729	26.750	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.010	0.003	0.0	0.010	-1	5	0.001	0.000	16.304	16.337	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.164	0.018	0.0	0.002	1	100	0.156	0.005	13.861	13.869	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.004	0.000	0.0	0.004	1	100	0.001	0.000	4.825	4.835	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.765	0.047	0.0	0.003	-1	100	0.156	0.005	17.712	17.721	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.002	0.0	0.006	-1	100	0.001	0.000	8.735	8.779	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.152	0.018	0.0	0.002	1	5	0.100	0.001	21.467	21.469	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.0	0.003	1	5	0.001	0.000	5.215	5.235	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.290	0.012	0.0	0.001	1	1	0.100	0.002	12.884	12.887	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	0.0	0.002	1	1	0.001	0.000	3.743	3.750	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=kd_tree.

WARNING! Mismatch between validation scores for 12 prediction (50.0%). The observed differences can be found in the diff_accuracy_scores column. The chosen difference threshold is 0.001 for accuracy_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	diff_accuracy_scores
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	fe8267e25dadb302ed498e50fabf7ef4	1eb6cf1a720a225efb91df0b529b0510	0.877259	1.077265	NaN	0.000091	0.000877	kd_tree	-1	1	0.929	0.130709	0.012519	0.910	6.711570	6.742282	0.019
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	dc74b969bf622bc24ba3bc62c980983b	1eb6cf1a720a225efb91df0b529b0510	1.134279	0.412869	NaN	0.000071	0.001134	kd_tree	-1	5	0.946	0.227291	0.008137	0.941	4.990419	4.993616	0.005
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	41accfcdcd5c50784bedf6164b63de99	1eb6cf1a720a225efb91df0b529b0510	6.121749	0.632406	NaN	0.000013	0.006122	kd_tree	1	100	0.951	0.714606	0.011837	0.940	8.566604	8.567780	0.011
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	022f7445d43bb1dbc24dc3106c03cb93	1eb6cf1a720a225efb91df0b529b0510	3.530933	0.233538	NaN	0.000023	0.003531	kd_tree	-1	100	0.951	0.688709	0.011866	0.940	5.126891	5.127651	0.011
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	84622ba45e941db642965553529e1941	1eb6cf1a720a225efb91df0b529b0510	1.854643	0.395971	NaN	0.000043	0.001855	kd_tree	1	5	0.946	0.235509	0.006124	0.941	7.875037	7.877699	0.005
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	a9455565db1d8e052a783317c99744ff	1eb6cf1a720a225efb91df0b529b0510	1.013470	0.380790	NaN	0.000079	0.001013	kd_tree	1	1	0.929	0.124144	0.003136	0.910	8.163690	8.166295	0.019
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	fe8267e25dadb302ed498e50fabf7ef4	edf7cb014c2c0910e1c3aaaf8ae633f2	0.033248	0.013085	NaN	0.000481	0.000033	kd_tree	-1	1	0.891	0.000665	0.000093	0.879	49.968679	50.454293	0.012
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	dc74b969bf622bc24ba3bc62c980983b	edf7cb014c2c0910e1c3aaaf8ae633f2	0.030908	0.001397	NaN	0.000518	0.000031	kd_tree	-1	5	0.911	0.000932	0.000058	0.905	33.150058	33.213179	0.006
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	41accfcdcd5c50784bedf6164b63de99	edf7cb014c2c0910e1c3aaaf8ae633f2	0.046444	0.012362	NaN	0.000345	0.000046	kd_tree	1	100	0.894	0.005953	0.000253	0.917	7.801923	7.808967	0.023
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	022f7445d43bb1dbc24dc3106c03cb93	edf7cb014c2c0910e1c3aaaf8ae633f2	0.049052	0.008449	NaN	0.000326	0.000049	kd_tree	-1	100	0.894	0.005924	0.000181	0.917	8.279923	8.283799	0.023
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	84622ba45e941db642965553529e1941	edf7cb014c2c0910e1c3aaaf8ae633f2	0.028991	0.002245	NaN	0.000552	0.000029	kd_tree	1	5	0.911	0.000935	0.000095	0.905	31.008564	31.169005	0.006
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	a9455565db1d8e052a783317c99744ff	edf7cb014c2c0910e1c3aaaf8ae633f2	0.026514	0.001769	NaN	0.000603	0.000027	kd_tree	1	1	0.891	0.000616	0.000036	0.879	43.070294	43.142123	0.012

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2.898	0.065	0.028	0.0	-1	1	0.885	0.137	3.274	3.313	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.877	0.053	0.021	0.0	-1	5	0.827	0.009	4.690	4.690	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.987	0.078	0.020	0.0	1	100	0.801	0.014	4.978	4.979	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.967	0.107	0.020	0.0	-1	100	0.832	0.027	4.771	4.773	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.004	0.054	0.020	0.0	1	5	0.791	0.007	5.061	5.061	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.958	0.068	0.020	0.0	1	1	0.830	0.010	4.770	4.771	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	0.019	0.0	-1	1	0.004	0.003	0.216	0.258	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.020	0.0	-1	5	0.002	0.002	0.332	0.400	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.022	0.0	1	100	0.002	0.001	0.480	0.567	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.023	0.0	-1	100	0.001	0.000	0.672	0.673	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.024	0.0	1	5	0.001	0.000	0.638	0.638	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.024	0.0	1	1	0.001	0.000	0.629	0.631	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.877	1.077	0.000	0.001	-1	1	0.131	0.013	6.712	6.742	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	1	0.000	0.000	10.424	10.683	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.134	0.413	0.000	0.001	-1	5	0.227	0.008	4.990	4.994	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.004	0.001	0.000	0.004	-1	5	0.000	0.000	8.123	8.417	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	6.122	0.632	0.000	0.006	1	100	0.715	0.012	8.567	8.568	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	1	100	0.001	0.000	3.413	3.504	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.531	0.234	0.000	0.004	-1	100	0.689	0.012	5.127	5.128	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.005	0.001	0.000	0.005	-1	100	0.001	0.000	5.841	5.912	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.855	0.396	0.000	0.002	1	5	0.236	0.006	7.875	7.878	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.000	0.000	0.002	1	5	0.001	0.000	3.284	3.558	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.013	0.381	0.000	0.001	1	1	0.124	0.003	8.164	8.166	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	1	0.000	0.000	3.556	3.622	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.033	0.013	0.000	0.000	-1	1	0.001	0.000	49.969	50.454	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.003	0.001	0.000	0.003	-1	1	0.000	0.000	23.486	23.901	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.031	0.001	0.001	0.000	-1	5	0.001	0.000	33.150	33.213	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.003	0.000	0.000	0.003	-1	5	0.000	0.000	14.778	15.658	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.046	0.012	0.000	0.000	1	100	0.006	0.000	7.802	7.809	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	100	0.000	0.000	4.933	5.042	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.049	0.008	0.000	0.000	-1	100	0.006	0.000	8.280	8.284	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.003	0.000	0.000	0.003	-1	100	0.000	0.000	16.548	16.788	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.029	0.002	0.001	0.000	1	5	0.001	0.000	31.009	31.169	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	5	0.000	0.000	4.860	4.972	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.027	0.002	0.001	0.000	1	1	0.001	0.000	43.070	43.142	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	1	0.000	0.000	5.691	5.724	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.625	0.084	30	0.026	0.0	random	0.325	0.010	1.924	1.925	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.694	0.029	30	0.023	0.0	k-means++	0.377	0.022	1.843	1.846	See	See
6	KMeans_tall	fit	1000000	1000000	100	7.769	0.342	30	0.103	0.0	random	4.197	0.026	1.851	1.851	See	See
9	KMeans_tall	fit	1000000	1000000	100	8.101	0.027	30	0.099	0.0	k-means++	4.449	0.075	1.821	1.821	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.000	30	0.008	0.000	random	0.0	0.0	7.490	8.841	See	See
2	KMeans_tall	predict	1000000	1	2	0.002	0.000	30	0.000	0.002	random	0.0	0.0	11.882	11.929	See	See
4	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.007	0.000	k-means++	0.0	0.0	9.164	9.506	See	See
5	KMeans_tall	predict	1000000	1	2	0.002	0.000	30	0.000	0.002	k-means++	0.0	0.0	10.721	10.924	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.001	30	0.342	0.000	random	0.0	0.0	7.300	7.559	See	See
8	KMeans_tall	predict	1000000	1	100	0.002	0.000	30	0.000	0.002	random	0.0	0.0	11.428	11.527	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.356	0.000	k-means++	0.0	0.0	7.235	7.434	See	See
11	KMeans_tall	predict	1000000	1	100	0.002	0.000	30	0.000	0.002	k-means++	0.0	0.0	9.850	10.793	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

WARNING! Mismatch between validation scores for 4 prediction (50.0%). The observed differences can be found in the diff_adjusted_rand_scores column. The chosen difference threshold is 0.001 for adjusted_rand_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	init	max_iter	n_clusters	n_init	tol	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup	diff_adjusted_rand_scores
1	KMeans_short	predict	10000	1000	2	3db9c030f488d0e8ff350ae449da4627	058b7e3842b587a5c675518f0706f5ee	0.002342	0.000185	20	0.006831	0.000002	full	random	20	300	1	1.000000e-16	0.000126	0.000705	0.000069	-0.000965	3.320606	3.336294	0.001090
4	KMeans_short	predict	10000	1000	2	ac9b8cec4749455c0d1cd1a92c150716	058b7e3842b587a5c675518f0706f5ee	0.002426	0.000185	20	0.006595	0.000002	full	k-means++	20	300	1	1.000000e-16	0.001245	0.000721	0.000085	-0.000750	3.365719	3.388827	0.001995
7	KMeans_short	predict	10000	1000	100	3db9c030f488d0e8ff350ae449da4627	7f85b913f395ec54101a6738ea63a9a7	0.003676	0.000192	20	0.217626	0.000004	full	random	20	300	1	1.000000e-16	0.278733	0.001609	0.000101	0.293767	2.284793	2.289254	0.015034
10	KMeans_short	predict	10000	1000	100	ac9b8cec4749455c0d1cd1a92c150716	7f85b913f395ec54101a6738ea63a9a7	0.003856	0.000724	20	0.207469	0.000004	full	k-means++	20	300	1	1.000000e-16	0.317011	0.001610	0.000080	0.256968	2.394554	2.397495	0.060044

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.113	0.007	20	0.001	0.0	random	0.058	0.002	1.950	1.952	See	See
3	KMeans_short	fit	10000	10000	2	0.326	0.004	20	0.000	0.0	k-means++	0.147	0.004	2.224	2.225	See	See
6	KMeans_short	fit	10000	10000	100	0.357	0.006	20	0.022	0.0	random	0.283	0.005	1.264	1.264	See	See
9	KMeans_short	fit	10000	10000	100	1.200	0.027	20	0.007	0.0	k-means++	0.649	0.004	1.849	1.849	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.000	20	0.007	0.000	random	0.001	0.0	3.321	3.336	See	See
2	KMeans_short	predict	10000	1	2	0.002	0.000	20	0.000	0.002	random	0.000	0.0	10.852	11.169	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.000	20	0.007	0.000	k-means++	0.001	0.0	3.366	3.389	See	See
5	KMeans_short	predict	10000	1	2	0.002	0.000	20	0.000	0.002	k-means++	0.000	0.0	10.569	10.668	See	See
7	KMeans_short	predict	10000	1000	100	0.004	0.000	20	0.218	0.000	random	0.002	0.0	2.285	2.289	See	See
8	KMeans_short	predict	10000	1	100	0.002	0.000	20	0.000	0.002	random	0.000	0.0	9.076	9.131	See	See
10	KMeans_short	predict	10000	1000	100	0.004	0.001	20	0.207	0.000	k-means++	0.002	0.0	2.395	2.397	See	See
11	KMeans_short	predict	10000	1	100	0.002	0.001	20	0.000	0.002	k-means++	0.000	0.0	10.356	10.581	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

WARNING! Mismatch between validation scores for 2 prediction (50.0%). The observed differences can be found in the diff_accuracy_scores column. The chosen difference threshold is 0.001 for accuracy_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	diff_accuracy_scores
1	LogisticRegression	predict	1000000	1000	100	f11be2529b0a77bc8fb5ee8de141e6b2	09c36ed6521e03974d0c134895f56c01	0.000523	0.000525	[20]	1.529044	5.232027e-07	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.56	0.000899	0.001335	0.55	0.581698	1.041321	0.01
4	LogisticRegression	predict	1000	100	10000	f11be2529b0a77bc8fb5ee8de141e6b2	7daecaea01e5baa8c862f264e3c1c3b0	0.003353	0.000706	[26]	2.385863	3.353085e-05	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.35	0.004047	0.000209	0.28	0.828616	0.829719	0.07

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	17.685	0.512	[20]	0.045	0.000	3.159	0.090	5.598	5.601	See	See
3	LogisticRegression	fit	1000	1000	10000	1.568	0.659	[26]	0.051	0.002	1.287	0.038	1.219	1.219	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.001	0.001	[20]	1.529	0.0	0.001	0.001	0.582	1.041	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.000	[20]	0.008	0.0	0.000	0.000	0.433	0.436	See	See
4	LogisticRegression	predict	1000	100	10000	0.003	0.001	[26]	2.386	0.0	0.004	0.000	0.829	0.830	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.000	[26]	0.711	0.0	0.001	0.000	0.123	0.124	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

WARNING! Mismatch between validation scores for 1 prediction (25.0%). The observed differences can be found in the diff_r2_scores column. The chosen difference threshold is 0.001 for r2_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	r2_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	r2_score_sklearnex	speedup	std_speedup	diff_r2_scores
1	Ridge	predict	1000	1000	10000	a70ae8dc5c059ec88cf27c61016aee2a	14fae294d530397144c65ba3209bf125	0.012924	0.000563	NaN	6.189802	0.000013	1.0	True	True	NaN	deprecated	NaN	auto	0.001	0.082567	0.021388	0.001215	0.122191	0.604288	0.605262	0.039624

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.327	0.007	0.244	0.0	0.337	0.004	0.971	0.971	See	See
3	Ridge	fit	1000000	1000000	100	1.467	0.100	0.545	0.0	0.479	0.216	3.060	3.356	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.013	0.001	6.190	0.0	0.021	0.001	0.604	0.605	See	See
2	Ridge	predict	1000	1	10000	0.000	0.000	0.903	0.0	0.000	0.000	0.576	0.583	See	See
4	Ridge	predict	1000000	1000	100	0.000	0.000	4.661	0.0	0.000	0.000	0.523	0.672	See	See
5	Ridge	predict	1000000	1	100	0.000	0.000	0.009	0.0	0.000	0.000	0.476	0.618	See	See